"Genomic approaches for dissecting fitness traits in forest tree landscapes" presentation by Ciro De Pace, Università degli Studi della Tuscia, Viterbo, Italy

1. Genomic
approaches for
dissecting fitness
traits in forest tree
landscapes
Ciro DE PACE
University of Tuscia, Viterbo, Italy

2. • The human footprint on natural ecosystems is now
almost inescapable.
• Pressures of habitat loss, pollution, overexploitation,
invasive species and climate change are increasing in
reach and intensity around the globe.
• Their cumulative effects are eroding biodiversity
and altering ecological processes, triggering
concerns that we are approaching a planetary-scale
tipping point.

3. • When the mentioned changes occur in forestsforests, the expected consequences are
severe for biodiversity and for the local people benefiting of forest services.
• The inability to move (‘sessility’) and the slow dispersal ability through seeds,
impede forest trees, in the short term, migration to the habitats that maximize
their fitness.
• One consequence is that ecological and genetic trapsecological and genetic traps rise when forest trees
remain in the habitat where their fitness is lower than in other available
options.
EcologicalEcological
traptrap
19751975 20092009

4. The purposes of this presentation
are:
• (1) describe the assumptions needed to measure
differential fitness in a forest tree population to
predict the genetic changes due to Natural
Selection and avoid ecological and genetic trapecological and genetic trap,
• (2) use a literature example to display the empirical
evidence for the existence of differential fitness,
• (3) develop a conceptual framework, based on
genomics, within which the fitness of forest trees
can be better understood,
• (4) propose some perspectives on genomic
approaches to improve fitness measurements in
heterogeneous environments

5. The main assumptions needed to measure differential
fitness are:
(1a) Differential fitness depends on Differential
reproduction
(1b) Aspects of the life-history of an individual
genotype affect its reproductive efficiency
(1) Genetic changes in a forest tree
population due Natural selection require
differential fitness among genotypes.

6. Differential reproduction is measured by the ‘reproductive
efficiency’ which provide a direct estimate of the genotype
fitness.
The fitness is often expressed as a relative, not absolute, measure
of reproductive efficiency. One component of the relative fitness
is plantplant fertilityfertility
1a) Differential fitness depends on Differential
reproduction

7. High fertility
Low fertility
The number of
female inflorescences
provide another
proxy estimate of
plant fertility

8. Seedling viability, rate of development, mating success,
etc., are life history traits affecting fitness and are known
as survivalsurvival fitness components.fitness components.
Seedling viabilitySeedling viability is the main life-history trait affecting
differential fitness.
Seedlings viability over time informs us about the strengths of post-fertilization barriers
and the local adaptation of the different seedling genotypes.
(1b) Aspects of the life of an individual genotype that
affect its reproductive efficiency

9. The general assumption for the
occurrence of differential
fitness among individuals of
the population,, is that::
There must be Genetic DiversityGenetic Diversity for
the plant trait putatively affecting
fertility and survival in the forest tree
population.

10. Estimate the individual net fitness in a
(Mendelian) population when genetic
diversity of the fitness-related trait occur at
one locus with two alleles
Individual genotype AA Aa aa
Number of seeds produced (fertility) 100 150 80
Fertility fitness 100/150=0.67 150/150=1 80/150=0.53
Number of seeds germinated 89 132 75
Number of seedlings survived 70 120 50
Survival fitness 70/120=0.58 120/120=1 50/120=0.42
0.67 x 0.58=0.39 1 x 1 = 1 0.53 x 0.42=0.22
Net fitness ω1 = 0.39 ω2 = 1 ω3 = 0.22
Mendelian population

11. Prediction on the effect of Natural selection in a
Mendelian population (1 locus, two alleles) with
differential fitness among genotypes
DIRECTIONAL SELECTION
AA
(ω1)
Aa
(ω2)
aa
(ω3)
Selection favouring the individuals with the
dominant allele 1 1 < 1
Selection favouring the individuals expressing
the recessive allele < 1 < 1 1
Selection favouring the individuals expressing
the heterozygous genotype < 1 1 < 1
Selection against the individuals expressing the
heterozygous genotype 1 < 1 1
Differential fitness among genotypes
of the Mendelian population

12. (2) Empirical evidence for the
existence of differential fitness
in Mendelian populations
(2a) Resistance of sugar pine (Pinus lambertiana)
to blister rust caused by the Cronartium ribicola
fungal pathogen

13. (2a) The resistance of sugar pine (Pinus
lambertiana) to blister rust (Cronartium ribicola)
display Mendelian inheritance
Kinloch Jr, B. B., Parks, G. K., & Fowler, C. W. (1970). White pine blister rust: simply inherited resistance in sugar pine.
Progenies of sugar pine completely
susceptible (right) and segregating (left)
for resistance to white pine blister rust.
Blister rust canker showing heavy resin
flow. Canker bark has been eaten by
rodents.
Early symptoms
Late symptoms
Spindle-shaped blister rust
canker on branch of a young
western white pine. (Courtesy
O. Maloy)

14. (2b) Because of scarcity of evidences
for genetic diversity for
morphological traits related to fitness
in forest tree populations, genetic
markers were used to identify
genotypes expressing differential
fitness
•Isozyme biochemical markers
•DNA genetic markers revealed by
(a) probe hybridization and (b) PCR
(Polymerase chain reaction)

15. IsozymesIsozymes are biochemical markers identified by electrophoretic
techniques.
•They are alternative forms of catalytic proteins encoded by different
alleles at the same locus.
•No more than 20-30 loci were possible to mark in one experiment, and
with few exceptions, alleles for isozymes were found adaptive-neutral.
•They helped in making inferences on demographic patterns and
colonization dynamics in several conifers and Fagaceae species
FS FS FF SS FF FF FS FF FF SS FF FS FFFS FS FF SS FF FF FS FF FF SS FF FS FF
SSSS
Electrophoretic pattern for Glutamate-oxalate transferase (GOT) isoenzymes
Superoxide dismutase (SOD)
Phenotype
Genotype
FS FS FS FS FS FS FS FSFS FS FS FS FS FS FS FS
Phenotype
Genotype

16. DNA makers revealed by hybridization to labeled nucleic acids
had the same limitations of isozyme markers
VNTR(Variable Number of Tandem Repeat)
(Multilocus but genotyping is difficult)
RFLP(
Restriction Fragment Length Polymorphism)
(Unilocus)

17. Multilocus DNA makers revealed by PCR were
promising for preparing dense linkage maps but
not to study fitness in populations
•AFLP (Amplified fragment length polymorphism)

18. Here is a stained PAGE gel displaying segregation for
three loci (a, b, c) used for preparing a linkage map of
maritime pine (Pinus pinaster Ait.) based on AFLP
Example of AFLP profile showing the three types of segregation. Lanes 1 and 2 correspond to the parents
(female and male) and other lanes correspond to the full-sib progeny. (A) Inter-cross marker, heterozygous
in both parents and segregating 3:1 in the progeny; (B) Test-cross marker, heterozygous in the male and
absent in the female, and segregating 1:1 in the progeny; (C) Test-cross marker, heterozygous in the female
and absent in the male, and segregating 1:1 in the progeny.
Chagné, D., Lalanne, C., Madur, D., Kumar, S., Frigério, J. M., Krier, C., ... & Brach, J. (2002). A high density genetic map of maritime pine based on AFLPs.
Annals of Forest Science, 59(5-6), 627-636.

19. Next Generation Sequencing
The recent development of next-generation
sequencing platforms has helped to revolutionize
population genetics by providing rich databases for
genetic markers that detect polymorphism at the
single nucleotide of the DNA template
(Single Nucletide Polymorphism, SNP)

20. SNP are codominant markers spread at million loci
within the nuclear genome
Morin, P. A., Luikart, G., & Wayne, R. K. (2004). SNPs in ecology, evolution and conservation. Trends in Ecology & Evolution, 19(4), 208-216.
Locus A
Locus nLocus 2Locus 1
n > 106

21. Several sequencing platforms are
available for SNP genotyping
Huang, C. W., Lin, Y. T., Ding, S. T., Lo, L. L., Wang, P. H., Lin, E. C., ... & Lu, Y. W. (2015). Efficient SNP Discovery by Combining Microarray and Lab-on-a-Chip D
Microarrays, 4(4), 570-595.

23. Normalized DNA plate
Double enzyme digestion
Adapter ligation
(24-plex inline barcodes on P1)
Purification
24-plex pooling
Gel-based size selection
(330-480bp, considering adapters)
Simulation on Vitis vinifera genome:
SphI+MboI @240-390bp
~20,000 expected loci
Amplification (Indexed primers)
de novo multiplexing sequencing of reduced
representation library of a tree genome based on
restriction enzymes and PCR amplification of the library
of fragments, speded-up genome –wide (GW) SNP
identification and genotyping.

24. Analysis pipeline forAnalysis pipeline for de novode novo SNP discovery/genotypingSNP discovery/genotyping

25. SNPs are used primarily for:
• Detecting population structure and measure
genetic diversity between populations
• Association studies for dissecting QTLs for fitness-
related traits
• Landscape genomics studies

26. 698 fixed SNPs, GTR model
HP1 - macrocluster
HP2 macrocluster
EVG-S and EVG-D
originated from
NOC x TGR cross
HP1 and HP2 are two hazelnut population at the
edges of a naturally regenerated deciduous
forest in Nortern and North-Eastern, respectively,
territory of the Latium region in Italy.
Detecting population
structure and measure
genetic diversity
between hazelnut
(Corylus avellana)
populations

27. Population structure, K=3
HP1 HP2
TGR
rosa unlabeled
Cultivated azelnuts
“Giresun”, turkish accessions

28. SNP genotyping are useful for fine mapping of linked loci
and for association for detecting allele associations at
loci affecting fitmess.
Morin, P. A., Luikart, G., & Wayne, R. K. (2004). SNPs in ecology, evolution and conservation. Trends in Ecology & Evolution, 19(4), 208-216.
Locus A Locus B
Equilibrium
(null hypothesis)
Linkage
disequilibrium
(D=0.1)
A B
a b
0.26 ab
Locus C
A
B
C
c
C
Genetic mapping
in experimental
poplations Linkage
disequilibrium
studies

29. Genome-wide association (GWA)Genome-wide association (GWA)
mapping to identify SNP molecularmapping to identify SNP molecular
markers explaining variation for fitness-markers explaining variation for fitness-
related traits.related traits.
The first case of GWA involving SNPs and a fitness trait,
is that of “serotiny” in Rocky Mountain lodgepole pine
(Pinus contorta) cones

30. Throughout much of its range, lodgepole pine (Pinus contorta Dougl.) produces
serotinous and non-serotinous cones. Serotinous cones do not open at maturity
because of a resinous bond between the cone scales.
Cones open and release seeds only n years when soil temperature reach 45-50 °C or
even higher due to wild fire.
Lodgepole pine seedlings emerging in five-
Serotinous
cones
Non-serotinous
cones

31. 51
non-serotinous
plants
47
serotinous
plants Locus 1 Locus 2
A A
A’ A’
Locus 97,616
SNP
locus
Depth
Minor
allele
freq.
Probability of
serotiny for the SNP
genotype
AA A'A A'A'
1 5.6 0.18 0.54 0.43 0.71
2 3.1 0.22 0.48 0.35 0.46
3 5.2 0.25 0.49 0.73 0.45
…..
97616 4.3 0.21 0.57 0.27 0.24
47 serotinous and 51 nonserotinous lodgepole
pines plants from three populations were
genotyped at 97,616 SNP loci
Loci 1 and 3
are associated
to serotiny
A
A’
GWA mapping of SNP markers for serotinous and non-
serotinous Rocky Mountain lodgepole pine cones

32. Rocky Mountain lodgepole pine forest is an example of
homogeneous forest where studying trait related to fitness ishomogeneous forest where studying trait related to fitness is
relatively easyrelatively easy.
Lodgepole forest trees occur as an even-aged, single-storied and
sometimes overly dense forest.

33. Often, forests display an eterogeneous forest landscape
composed by a tree community on a spatially variable pattern
In a complex community of forest trees, independent measure of survival and fertility is
difficult to achieve separately for each species.

34. Forest fragmentation, mixed land cover, and
patches of invasive species, reduce forest
connectivity, increase the ‘island’ population
structure, and new evolutionary forces come into
play such as drift, assortative mating, increased
inbreeding, reduced dispersal (migration) ability,
etc.

35. The geographical distance in the
ecosystem cause differential
ecological processes in the forest
landscape
Seed predator
differentiation
Occurrence of
invasive species
Differential impact of two or more stressors on
the ecological response (e.g. diversity,
productivity, abundance, survival, growth,
reproduction) of the plant community.

36. LANDSCAPE
ECOLOGY
• The forest landscape in heterogeneous environment may
be envisioned as a mulilayered structure where the
ecological landscape is one of the layer.
• Sophisticated approaches are needed to quantify spatial heterogeneity, particularly
through the integration of geographic information systems (GIS) into the analysis of
ecological processes in the territory.

37. LANDSCAPE GENETICS
Landscape genetics endorses those studies that
combine population genetic data, adaptive or neutral,
with data on landscape composition and
configuration and spatial heterogeneity.
In landscape genetics the phenomenon under consideration is the genetic structure of
a population and the processes that govern it, such as gene flow or adaptive evolution

38. PEPE
GENETICGENETIC
SS
Each individual population is
represented geometrically
as a node whose volume is
proportional to the within-
population component of
genetic variance at several
loci associated to fitness
traits.
The intensity of
interpopulation ‘genetic
differentiation’ (FST) may be
interpreted in terms of
features of the ecological
landscape.
Pop 2
Pop 3
Pop 1
Pop 1
Pop 2
Pop 3
FST
FST
FST
Geographical distance;
patchiness heterogeneity
Population geneticsPopulation genetics
Landscape ecologyLandscape ecology

39. FUNCTIONAL TRAITS
• In more complex situation, a more productive way of
asking the classic question of what processes maintain the
diversity of species, is to ask: what processes explain thewhat processes explain the
dispersion of traits among community members.dispersion of traits among community members.
• Therefore, one way to make sense of this diversity and its
mechanistic underpinnings is to focus on the functional
traits they possessed by species, such as plant height,
seed size or leaf area.
• It has been demonstrated that functional traits
consistently pre­dict the competitive interactions
between trees in six forested biomes, and help in
deconstructing the fitness landscape in plant
communities.

40. S Díaz et al. Nature 1-5 (2015) doi:10.1038/nature16489
The global spectrum of plant form and function.

41. Chemical fingerprinting of metabolome byChemical fingerprinting of metabolome by
infrared (FTIR) spectroscopy is promising ininfrared (FTIR) spectroscopy is promising in
identifying biomarkers of fitness in complex forestidentifying biomarkers of fitness in complex forest
communities.communities.
• FTIR is has been used to identify Quercus agrifolia plants
resistant to Phytophthora ramorum, the causal agent of
sudden oak death, prior to infection.
• Concentrations of quercetin flavonol and ellagic acid
phenolic dilactone were foud to be higly significant
biomarkers of resistance.
• Therefore, chemical fingerprinting can be used to identify
resistance in a natural population of forest trees prior to
infection with a pathogen and speed-up discovery of
candidate genes for fitness traits.

42. PERSPECTIVE
In the near future, deep genomics coupled to the
assessment of “functional traits” and “fitness
biomarkers” will promote precise genetic dissection
of fitness traits in the forest tree landscape.

43. THANK YOU

44. Deep genomics to explore the
fitness landscape.
If the cost of resistance is large, compensatory
mutations will sharply increase in frequency. This
prediction can be tested by determining the LD
decay for polymorphic SNP in genomic regions
harboring expression QTLs and structural genes
associated to the resistance phenotype and its
biomarkers
Fitness
a
Genotypic space for
resistance
Genotypic space for compensatory
mutations